{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--BOOK_INFORMATION-->\n",
    "<img align=\"left\" style=\"padding-right:10px;\" src=\"figures/PDSH-cover-small.png\">\n",
    "\n",
    "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n",
    "\n",
    "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Multiple Subplots](04.08-Multiple-Subplots.ipynb) | [Contents](Index.ipynb) | [Customizing Ticks](04.10-Customizing-Ticks.ipynb) >\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.09-Text-and-Annotation.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Text and Annotation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a good visualization involves guiding the reader so that the figure tells a story.\n",
    "In some cases, this story can be told in an entirely visual manner, without the need for added text, but in others, small textual cues and labels are necessary.\n",
    "Perhaps the most basic types of annotations you will use are axes labels and titles, but the options go beyond this.\n",
    "Let's take a look at some data and how we might visualize and annotate it to help convey interesting information. We'll start by setting up the notebook for plotting and  importing the functions we will use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example: Effect of Holidays on US Births\n",
    "\n",
    "Let's return to some data we worked with earler, in [\"Example: Birthrate Data\"](03.09-Pivot-Tables.ipynb#Example:-Birthrate-Data), where we generated a plot of average births over the course of the calendar year; as already mentioned, that this data can be downloaded at https://raw.githubusercontent.com/jakevdp/data-CDCbirths/master/births.csv.\n",
    "\n",
    "We'll start with the same cleaning procedure we used there, and plot the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "births = pd.read_csv('data/births.csv')\n",
    "\n",
    "quartiles = np.percentile(births['births'], [25, 50, 75])\n",
    "mu, sig = quartiles[1], 0.74 * (quartiles[2] - quartiles[0])\n",
    "births = births.query('(births > @mu - 5 * @sig) & (births < @mu + 5 * @sig)')\n",
    "\n",
    "births['day'] = births['day'].astype(int)\n",
    "\n",
    "births.index = pd.to_datetime(10000 * births.year +\n",
    "                              100 * births.month +\n",
    "                              births.day, format='%Y%m%d')\n",
    "births_by_date = births.pivot_table('births',\n",
    "                                    [births.index.month, births.index.day])\n",
    "births_by_date.index = [pd.datetime(2012, month, day)\n",
    "                        for (month, day) in births_by_date.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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Gj1hIRUVRuTIpD64Ykwn47DPgr38Fhg2jiHFFhfJr/fILRWVGjaJkPzmuXgXOnDE32xg8\nmBLn1LwOACxZQmK6WTPg4EHyEHbvTlFSOWHcpo2lMD5/nsS8rf0FlISxyURCOC6ObgcEkEVk1ix1\n41GLVPKdmPBwoLiYbCl1HTEGaOLDPmOmLjl3jiZpYWEsjBmGYRo8YmHs50dRRKWI6fnzdDEZOBBo\n2ZIsCGoinz//TN3X7r+fLBVy9UIzM0nICh3cmjenC9fevcqvk5lJCW4vvEBVI95+m2wU3bqRMDQY\nzPuWl1PEW4hMiyPGGzYAMTHA009b7n/oENXhFRMXR++BUCPYmtOnKflNLEb/9jeK3v72m/KY1FBe\nThOKkBDb+2g05DPessU1hHFcHFULYZi64sAB+j1rNCyMGYZhGjzWS+/t2yvbKQoKKBIrJJ8NGwas\nXy//GJPJLIybNgU6dbJdOg2wtFEIDBqkXM+4tJQi2amplBT34ovAunXADz+Q0A4Pt4wYL1xIY7n7\nbrotCOMff6TWsIsWUTtlIVJ9/DgJSp3O8nXvuIN8ykKjEGsEYS5GqyUB/+9/y49JLZcvU11iT0/5\n/aKjqSKGKwjjoUOp+cj583V9JExD5c8/zRNd9hgzDMM0cKyrGKjxGRcWWi7XDxum7DM+eJCiv+3a\n0e3hwylqbAspYTxggHJ08Z13gIgIc9vlkBASuLt3myPGgjDOzQXmzqUayYLIb92aROykSeSjfuQR\nEs6HDtH9UjYKATk7RVZWdWEMkEd7/XqyN9QUpYoUAu3b09+69hgDFKF78EFgxYq6PhKmoXLgAK0M\nARwxZhiGafBYe1LVCmOxABs4kC4uly7ZfsxHH1HCmSBABw6Ub4ohJ4xtlVM7cwZ47z2KwIobV7zw\nAiW9tW1rKYxTU4FnnjGLdYCE8Y4dFNkeMoS29e9vFrxywnjgQIpOS7F/v/TjGjWicdVGww+lxDuB\n9u3JNnPHHTV/zdogJYXaVTNMXSBYKQAWxgzDMA0eazHVpYtylQBrMe3rCyQkAJs2Se9/+TKwbBnw\n7LPmbX36kFi8caP6/ufPA0VFloIVID+zjw9w7Jj068yYQa8RGWm5PTychK2nJ9kHBI/xb78BDzxg\nuW9kJFXbeOcd87Z+/ajEHEBWjh49pF//0Ufp2KSqZ9iKGANAUlLtlG5TSrwT6NyZ3su67HonZsgQ\najhS0zrVDGMvlZW0GiQk+bIwZhiGaeBYC+N+/air2759th8jtWQv5zP+5BPy+4qX7v39SYRLVSTY\ns4caYUgJt4EDKWp89CgwdGjjKu/vjh0kWmfMsH3cAFkrSkuBs2fJStG9u+X9Pj7A2rWWCWyCRWL7\ndir7Nny49HP7+FCi39SpltUzjEaKZkdHSz9u5Egak5raznKojRh36+ZaCW+enjQ5WLmyro+EaWjk\n5tJkUlg9YY8xwzBMA8daTHl4kCf3s8/kH2MdmRSEsbXNoaIC+OADsjNYY8szLJRWk0J4zLRpwIED\nWhw4QNvXrgUmTKCIjxwaDUWNv/6aKiJ4e8vvD1A06dw5SuSbMYOS5mwxahQQFEQRcoGDB8m+YOu1\nAgPp/aupMFQrjAH1+90uBg82R+UZ5nYhtlEAHDFmGIZp0FRUkM0hNNRy+/jxtLR/86b046Qixu3a\n0UXFuirDiRMkCKXsB7aEsVQFB/FjVq0iH/RDD13H9u20fetWiiarITycROiAAer29/Qk68eJE1Tx\nQg6NBnjqKUvPsNx4BJ54Avj8c/PtkyfVHZsYtcl3rohQz7iy0nI7d8VjnAkLY4ZhGKaKK1couinU\nChaIjCQh++230o+zrkohIGWnOHqUEvqkiI+nKKF10w45Idm5My13vvce0LdvKbZto3rI+/aZm2co\nodeT9UKtkAYoQezdd8lPrcTgwUB6ulnkySXsCdx9N7XZ3r+fSsW1a2e7JrIt1HqMXZGmTanUnLiZ\nSlYW1a4WJj8MU9uwMGYYhmGqkFt6HzsWWLPG9uPsEca2vLVNm9I/cam3GzeAU6dsi2kPD4rcPvAA\nEBtLwjgzk6wK1rWFbREeTlFgtUIaAMaNM5eAUyIigjzKgs1DTcTY05Oixh98QJUyPDxIKNuDPVYK\nV6RPH5qwAGTJef554M47qdazrUokDFMTDAagRQvzbfYYMwzDNGDkhNSAAbZr8tpash80iCpaXLtm\n3nbkiG1hDFD1h2efpeQ8o5HEZHS0vI83MJD+tm9fjvx8si2otUUAJIx79FAvpB1h8GDg119J5B88\nWL30nBTjx5O3OyGB6qqeO2ffa7qDMN65k/7/3XdUneTHH6mbX22Us2MYay5ftky0VRMx9pK/mxg1\nahR0t84wERERmDRpEl5++WV4eHggKioKs2fPBgCsWrUKK1euhLe3NyZNmoRBgwahpKQE06dPR2Fh\nIXQ6HebNm4cQuX6WDMMwjCLvvAOcOhWI7t2pLbIUcp7Utm3JY3z2LEVAxdiKGAcEUAWHzZup5BlA\nEeNHH7V9nCNHUqT5oYeolq2Xl3J0VcDTkwTnxx8DixerewxAr2dd0q22GTyYmlbk5FBSYFCQ8mNa\nt6ZOe4mJwOOPUwkze5pwuIMwXrWKhMnUqVT72scHeOstSnwcOdJ1Sswx7sGlS/YLY8WIcWlpKQBg\nyZIlWLJkCd58803MnTsXU6dOxVdffYXKykps2rQJBQUFSEtLw8qVK/Hpp58iNTUVZWVlWL58OaKj\no7F06VKMHDkSixYtqtEgGYZhGjo5OVQ2zN/fhOees72fnJDSaKQ7uZWXU0TYVvzC2k6hFDEGSPy8\n9BLw/vvkFVYrjAFzebn4ePWP6dgRePhh9fs7wqBB5DNOSyNLgFqefpo+k7Aw2xHjPXuozrM19Tn5\nDqASfX/+SSI4Pp6arAD0naqo4KoVTO1jnXxcK8I4Ozsb169fx4QJEzB+/Hjs378fhw4dQq9evQAA\nCQkJ+OOPP5CVlYXY2Fh4eXlBp9MhMjIS2dnZyMzMREJCQtW+22yt3TEMwzCqWL2ayoY9/zz1GbbV\nblgpwti3b3VhfPky1fz09JR+jLhsW1ERLYOHhysf86BB9JxffWW/MI6MVPcat5PmzSnJb/Ro+msv\nUsK4tBR4+WWgVy/gyy8t7xMmLMHBjh9zXRMQAERFUem9hQvN2zUairrLlRBkGHspKaEEV3GJx1rx\nGPv6+mLChAn47LPPMGfOHLz44oswiVzyAQEBKC4uhtFoRKBgDAPg7+9ftV2wYQj7MgzDMI6zejUJ\nMoCS22wlcSkJY6mIsVJUsmNHqsZw+DB1gYuKokQyJTQaYMoUEtL2COOhQy2T91yJRYuA115z7LFS\nwnjSJErkmzu3+uciLAmrea9dmYkTgSVLqpcQTEkhwSwVKWcYR7h8mSaSYntOrXiMIyMj0apVq6r/\nBwcH49ChQ1X3G41GBAUFQafTWYhe8XbjraOwFs/WGIQ+nm5KUVGR243RHcdkjTuP0Z3HJuBuY8zL\n88CRI03Rvv05FBUVISSkFAcPXoWvb/XaX6dPByMsrAQGg0RfZgARERrs398Mubnn4OND244c0SIw\nMAgGg+0CsyNGBOH994GuXcvQooUvDIbLqo59yBANpk8PQGlpMdR8JEVFRQAMCAqCqv1vN+3b0yTB\nkWPTan2Rm+tX9f3ctcsbP/0UioyMC8jP98S//x0Kg8E84zlwwAuhoSEwGC7W4gici9RvT/CmS71n\ncXEh+PjjEiQlKYT0XAh3O7+Iqe9jO3bMC0FBlr+Zigrg+nV5Y7+iMF69ejWOHj2K2bNn4/z58ygu\nLkZ8fDx27tyJPn364LfffkPfvn0RExOD+fPno7S0FCUlJcjJyUFUVBR69OiBjIwMxMTEICMjo8qC\nIYXekfWoeoTBYHC7MbrjmKxx5zG689gE3G2MX39N4qJVKz0MBgPCw7UwmZpILudfvw60a+cPvd52\nwnP79sC5c3r062feRjYB2+/ZSy/Rcn9gIHWw0+v9VB//228DgIpMNbjfZyemUyfyJwcGBqJpUz3m\nzAHmzweio5ujXTuyx3h46BEWRvt/8QV5cuvT+2Hv5zdpErBwoR+mTas/fhF3/o7W97GdPAk0aVL9\nNyMEAWyhKIwfeeQRzJw5E8nJyfDw8MC8efMQHByMV155BWVlZWjbti2GDRsGjUaDlJQUJCcnw2Qy\nYerUqdBqtUhKSsKMGTOQnJwMrVaL1NTUmoyTYRimQbNuHTB5svl2s2a2rRTCUqIc8fHAli2oEsZq\nErwiI6nk2H/+Q/8Y+xFbKb79lpZ4H3uMbnt4mP3fQhLh6tXU9MSdufdeslRcuULfW6ORRIx1gxqG\nUYN1RQoBf3/5xyl+3by9vfHuu+9W256WllZtW2JiIhITEy22+fr6YqHYZc8wDMM4zNGjll3W5DzG\nxcXmmsC2GDqUIpUvvUS31XZXe+458oQqVaRgpBEL49276XMQeyEF//fDD1MVkrNn7evkVx/x96cJ\n14YNNEkYOZIar4wbV9dHxjgbk6n2S/VZV6QQECfjSVHPbfwMwzANh9JSElPiTk5KwlipycWQIcCu\nXeamHWpLgiUkANOmWbZbZdSj05EYMBo1ki2lxYmRq1eTfcZWpRB3YsQI4IcfqKzbL78Aubl1fUTM\n7WDECMFmZaasjLpgOoqtiDELY4ZhGDfh1CkqWyZeWm7alDqISWE0Kl8EAgKA/v2BTZvottqIsUYD\nvPuu8vMz0mg0FDW+cMEDf/5ZXRj36UP1jHfvpkYiQhUSd+f++6kKyfz51BDl7Nm6PiLG2Vy7Bvz2\nG7VLF3dA/PBDEsyOwhFjhmEYNyc3F2jTxnJbTSPGAHDffeaSaPW9iUR9IiyMMuevXgVuFX+qIiiI\nrARPPUWdCQcPrptjvN20bEnJn8uXA6+8AuTl1fURMc7m11/JU79mDfC3vwFZWcCNG9TdMyeHIseO\nYN0OWkBJGLOlnWEYpp6Qk6NeGNMyvbqI7n330TKmyUTCWE3EmKk5YWHAb7/5oEsX6frEy5bd/mNy\nBUaPpkhxz57AggV1fTSMs1m/njz2vXtTd8wHH6QkzN69yVKTm+tYLsOlS/QdsqbGyXcMwzCMa5CT\nQ8vLYmwJ4xs3KKNfjS81KoouFq+9Bpw5w8L4dhEWBmzY4IO7767rI3Et/vEPmqRdvmzbSlFWRt9V\n64kiU78wmSjZUrBQjBkDHDwIvP462YheeUVd23kp2ErBMAzj5khFjBs3pshIRYXldrXRYoGVK2nZ\n2s/P9dovuythYcCJE96IianrI3EtPDxoQteoEU3wpFr4rlxJHRQzM2//8TG1x/Hj1LpZnMT76qvA\n1q1AbCzVWT9yxLHndtRKwcKYYRimniDlMfbyopqvly5ZblfrLxbo2RNYvJhaPSvVPmZqB6F5h3Xi\nHUNoNIBeL+0zzswkX+oDD5C4YuonGzZQ/WpxqTYPD6qvDgAdOgDZ2Y49N1elYBiGcXOkrBSAtJ3C\nXmHM3H6aNaO/HDG2TUSEtJ1i715g+nRK1mIfcv1l/34gLs72/TWNGEtZKZQ8xiyMGYZh6gGXL5Nd\nQsr/K1WyjYWx66PXA+Hh5RyhlyEionrEuLIS2LcP6NGD6j0fPVo3x8bUHINB3rrlqDAWPOocMWYY\nhqmHXLigXJJIsFFIdYeyFTHmGsOuTWwssGJFYV0fhksTHl49YpybSx0dmzShxNFjx+rm2JiaYzDQ\nBNEWzZsDN2+SyLWH69fJkuHrW/0+FsYMwzAuzuDBFBn58kuKdEhhy0YBSAtjo5Ejxq6ORgO0aVOh\nvGMDRrBSmExU17akhGwUQhmuVq2oG2RJSd0eJ+MYSsJYo6GKFPZGjW3ZKAAWxgzDMC7NqVMkar/4\nApg1i0oUSSFVkUKgWTP2GDPuiWClyMwEXnoJSEujjoA9etD9Xl7UFCQnp26Pk7GfsjISsE2byu/n\nSAKeLRsFwB5jhmEYl2bDBipuf+edwIQJwNKl1fcxmYBffgE6dpR+Dk6+Y9wVwUqxahUwZAgwbx5N\nHgVhDLCdor5y7hydu5RqrTviM7ZVkQLgiDFzmygvB959t66PgmHqH0LXJwAYOxZYsYJ+T2I++QS4\neBEYN076OVgYM+5KRAQ18li1iqpPNG8ObNxo2dGsXTsWxvURJRuFQJcu1CbaHthK0cAxmeq+yPnp\n01Q65/Rroa+yAAAgAElEQVTpuj0OhqlLbt6kC/jJk+r2LysDNm+mOp4AeelatKBtAocPk8Vi6VJA\nq5V+npAQ6TrGnHzH1HfCwoCCAmo806UL/RaaNSPBLKA2YnzpEpCf77xjvZ1cugRMnVp9El2fyMtT\nJ4z79AF27LCdfyEFR4wbOAYDMGhQ7T5nWhpQWqp+fyFr+Jdfavc4GKa+kJ5OovbFF4EPPlD3mO3b\ngbZtzfVsAYoaf/klXfA2bqTf9nvv2bZRANT62fr3ysl3jDvg6Uni+NFHKRFr2DDym4qrs6gVxs8+\nC0yb5rxjvZ3Mng0sWlS/azirjRiHh9PE6MQJ9c8t5zFWannPwtgNuHKFokM3btTO823fTku2e/ao\nf8zZs4C3NwtjpuGyfj0weTLw9dfkG1aD4C8Wk5REHsqAACAlhSLQKSnyzyMljNlKwbgLiYmWNiLr\nus9qhPGZM8D33wMZGfZFHh3h55+BoiLnPf/Bg9QSOyODPNdqV6hcDbXCGKAmIDt2qH9uOWGs1FCH\nhbEbcPUq/b14sXaeb948+kLZI4zz8oD77ydh7OyTDsO4IqdOUfS3Vy9arpXq1mVNejolFIlp1owS\nTYqKqF7rnXcqP49WW71cFQtjxl2YP59+W7Zo2ZKuf3LBoX//G3jySfq/PZFHezl3DnjwQeCrr9Q/\n5ocf6LxhbYeyxfTpZCmJiyM7xZQpjh1rXaPU3EOMvcK4JitmLIzdAEEYWyffOMKhQ8C2bcArr9jn\nWz57FkhIoGLahw/X/DgYpr5x6hQQGUlLv3ffrRw1vnGD6rH26yd9v1ZLy4dq0GqlI8bsMWYaAp6e\n9NuzJXiNRuDTT0lA3nkn8NtvzjuW+fPpWNasUbf/K6/QStPNm8CWLcr7G400oZ40iW5Pm0ZtlX//\n3dEjlubsWWDWrDtw8GDtPq8YeyLGffvaJ4xv3qSVNEdgYewG1GbEODWVTh4DBtgvjCMigLvucq6d\n4uhR4KefnPf8DCNm8WJgzhx1+548Sc0GALJHiIXx+fNAfLzl/jt3UjJRbYhXW8KYI8ZMQyEqimyA\nUnz3HUUc27QhYZyRof55T52iLmpquHyZBPjatfT7VooAV1RQJHvrViA5WZ1gz8oCOnc2iz4fHzpH\nzZxZu6u1mzYBW7ZocdddpAucgT3CODYWOHCABK8aSkqku96pQZUwLiwsxKBBg5Cbm4vs7Gw89thj\nGDt2LGbNmlW1z6pVqzB69GiMGTMG6enptw6sBFOmTMHYsWMxceJEXLa3px8jSUYGXWSFmZxSxPjH\nH8mHrIZffwVGjwa6diURqvZLKBbGmzape4wjrFsH/Otfznt+hhHz9de0zKlESQlQWGg+yQ8dSr+D\niltNzbZtA/74w7Kt6ZYtwMCBtXOcnHzHNHSmTwf+8Q9KSqustLxv0yZg+HD6v1phnJkJ9O9PYlpt\nMu1HH5GNomNHskitWye//759VH4uPJxWXNUIY3FzE4GUFAqMrV+v7jjVsH8/kJR0HUuX0sTCGdgj\njP39qWrPvn3q9i8pcWLEuLy8HLNnz4bvLen94YcfYvLkyVi6dClKSkqQnp6OgoICpKWlYeXKlfj0\n00+RmpqKsrIyLF++HNHR0Vi6dClGjhyJRYsWOXaUNcBkAkaOrL3EtLrEZKKM95QUKl9z9Chtl4sY\nX7wIPPII8L//KT//tWsU2YqKoplWVBTw55/qju3sWfpxDx1KyzxGo7rH2UteHiUmqZ3BM4yjGI20\ndHfokPL3+cwZOsELherDw+nfrl10e+dO+nvokPkxW7bQxbA2YI8x09AZOJCsSStXUoBHQGiOc/fd\ndLt9e9IDp07JP98rr5B2WLVKfbDnjz9IGAPAww9T5FiO9HRzRanevcmGqJS0t3dvdWHs5UXtsidP\nrr2kv337gE6dytGtG0Wpazt36MYNuo7bqjUsRZ8+tjuDWuNUYfzWW28hKSkJTW/17OvUqRMuX74M\nk8kEo9EILy8vZGVlITY2Fl5eXtDpdIiMjER2djYyMzORcOvMn5CQgG3btjl2lDXgwgUShfa2E3RF\n3nmHsl2zsihiLCzTXL1KF0CpiPEHH9DFWs0sS1iiES7usbHq7BTl5STAmzenL3nfvs6zO+Tl0evZ\n4zViXJ+PPqoe5alNHKldmp5OCTExMconY8FfLEZsp9i5k4SzIIzLyymKbG2vcBS2UjAMlXUbPtzy\nunXiBP3e2ren2xoNTUjlosanTtFvdsoUWgXdvr36xFOKEyeo2QgAjBhBglwuiPPrr8DgwfR/Hx86\n3/zxh/xr7Nlj2dxEYMQIeq7aSMQzmShi3KlTGRo3JruX0kQCoBUytWVehWixuOyeEq1akQZQg9OE\n8Zo1a9CoUSPEx8fDZDLBZDKhVatWeOONN3D//ffj0qVL6NOnD4qLixEYGFj1OH9/fxQXF8NoNEJ3\n68wcEBCA4uJix46yBgiCuL4nhG3cCHz4IdkigoNJgIqFcbt21SPGxcXAf/5DVSb27lV+jf37gW7d\nzLdjY9VVpjh3DmjcmMq1ARSh/vprdeOyl7w8mjWqSVJg6geXLwNPP63uxOsIBw54ITLSvMKilvXr\nqWZqfLxyYsupU2Z/sYAgjCsrSVj/5S9mYbxvH2XSK9XTVIutcm2cfMc0NHr2tLxubdpE0WKxAFOy\nU3z+OXl+/fzoetupE01k5aiooCoybdrQ7dBQup5u3Sq9f3k53SeuOqNkpygtJU1jq9zYggX0nGrL\nRdri7Fk6pzRpQtGKrl1JHyjxxRekRdTEQO2xUQhIdfi0RU2S77zk7lyzZg00Gg1+//13HDlyBDNm\nzMDhw4fx3XffoW3btli6dCnmzZuHgQMHWoheo9GIoKAg6HQ6GG+tQRqNRgvxLIXBYHBsFDLs2OEP\nIBg7dxZh0CB1awwmE1BQ4FH1pagtioqKHB7jt98GIinJBA+PYhgMgJeXDqdPa2AwFCE/Pxjh4Zpb\nt81u/88+C0BcnBYDBlzFrFlNkZd3TnZ2tm3bHejUqQwGA01xW7b0xuLFd8BgKJAd09GjF9GsmXm/\nvn09MG1aU5w4cR5+frW7/nL6dFM88YQRmzb54sknC2v1uW1Rk8/N1XGFsWVmegNogi1bCuHjoyIs\nYyebN3vhjjsqMGFCOVasKFQdofjhh6b46KNLOHXKCytX+mP8eNuZNH/+GYjQUMBgMJ9j2rYFsrLC\nsG5dIYKDQ9C581V8/nkADIZL+N//AhAb6wWD4WqNxiZ8fiYTUFamx9mzBnh4CPeFoajoPCoq6m/9\nRFf4fjobdx5jXYwtIsILO3eGwmAgBbVuXQjuuecmDAazn7JjRy+kppr3EVNRAXzySTMsWVIIg4Ha\nyvXpE4hvvwWio6trCGGMeXmeCA5ujKtXz1fZG+PidPj2Ww906XKt2uP27fNGWFgwyssvQniLOnXS\nYv78QDz7rPS17cABL7RsGYIrVy7azBu6554gZGRUIibG8UDkL7/4oEOHgKqxtW0biK1bTejdW/45\nd+0KQnS0Fx54wBtPPmnE008XVwXMTCZg1y4t+vQpvTUWX4SE+MFgUJ975uXlg9OnAyx0ji2Kihqh\nuLgIBoMdncqE15G78ytRIb5x48bh1VdfxbPPPlsVBW7WrBn27t2LmJgYzJ8/H6WlpSgpKUFOTg6i\noqLQo0cPZGRkICYmBhkZGejVq5fswejtnT6o4Px5oHt3IC8vEHq9vDAX+OYb4K9/JR+vrRasjmAw\nGBweY14eRZz0+iAAQOvWtFyk1weirIxmdJs2Wb6Hv/9Okbhu3fzg708XTuvlXjHHjlEJGL2eqqff\ncQdtCwvTV11spcZUUtIErVubX1uvJ79UVlZzPPyw7de7dAl4/XXybw4bBtl9AfphnT8P/O1vd+C9\n94AmTfRVPzpnUpPPzdVxhbFt3Eh/L1xoZHcEQQ179tzE/PmeePddT2Rk6JGcrPyYnBzywN1zT1Oc\nOwe8/LL87+DSJYr8WJ9jBg4EPvmkCfr1AwYObIRZs+h3snUr8MILgF5fs5Cu+PPTauk34eNjXtJs\n06a5zWOuD7jC99PZuPMY62JsTZvSdUKn00OnIxvE4sV+0OvN3R7CwsiLazLpq9XR/flnSiS/++6m\nVdtGjqSkPikNIYzxyBHKyxGP9+GHgb//HdDrdaiooFXdsDC6b+lS4J57LPcfMQKYMAFo1Ih+xyYT\n/Za9bim19evp2ir3nrZoQcn2glZwhLNnqYpHYGAg9Ho94uOB1auVn/PCBdIQvXsDEycG4aGHgvDj\nj2SzPHCAGrXcvEl2zevXKbqs16usSQmgQwf63NR8p0wmIDzcx+Y1JV/GX2f3KfP111/H888/j5SU\nFCxfvhxTp05F48aNkZKSguTkZIwfPx5Tp06FVqtFUlISjh07huTkZHz99deYPHmyvS9XY44cAR56\nSL2V4sYNSnDz9a39uoA14ehRs0cKkLZSiJcYKivJIxUXR7d79JD3GVdUUJWLrl3N2wICqNGH0oQ/\nL696ke5Ro5QzWbduJQ9Wp07A3/5GJzM5CgtpaSs8nPycauwhjOuTnU0nc3FiWm1RUQHs3Eklh+bP\np4mYmiSS3bupvrBGQyf1O+6Qt2JIeYwBmvCtW0f2n1at6Dd75gw9/113OTwsScR2iuvXKYu7Poti\nhnEELy+6ju3bR3YJvb769cnDgyatUraFb7+lykxi4uMpB0cuse34cbO/WKBPH/IdFxRQjlBcHFko\nTCZg+XJqiiVGpyPxJ1hBFiwAHn/cfL8tf7GYkBDL6jeOsG+fpa2yWzd1VoqcHLKStGpFeUZdugDL\nltF96ek09nPn6PaZM3Tet4cmTdRbKZyafCewZMkStG7dGj179sTy5cuRlpaGzz77rEq5JyYm4ptv\nvsHq1atx9630T19fXyxcuBDLli3Df//7XzSqLUOdHRw5AjzwgNmAr0RqKhngJ060z6dz/Dj5a5xB\neTl94cQ/OrEwvnKFZqpij/GxY0BQkHl22qOHvJA8fpxm2kFWE8K2bZW7BAml2sQMHKjsMzp+nDJy\nn3uO2n3+85/y+4u75Nx5p/0Jfk89ZV/9Sub2cOQIRVackQfw559Ao0aVCAuj78z169UrrSxYYD5Z\nC4i9ggDV9f75Z9uvI+UxBsztnvv0oYtxhw70egMHknCtTcSVKdhfzDRkBJ/xv/9tboRhjZTP2GSi\niewDD1hu9/MjcSxX3enEierd+by9yTe8ejVpCz8/eo7t26kK1L33Vn+efv3MCXjr1lFVjDNngLIy\nOgcJwS5bhISoL89qi/37aaVdIDqarr9yaWImk1kYAxRUGD3aXEJOeK9Pn6a/jghjezzGt0UY10dK\nS+nN79KFoj45OfL7FxXRl/edd6oX6Fdi2jSKRqmhrIySx/bvV1cn+ORJErjiLljWEePwcIoSC2Wl\nduyg6hAC3bvLR4ytE+8E2rRxTBh37kyVAAplbMDiGfY//kGlbeTKw4kj088+S8mIak8ApaU0c3VW\nPUbGcbKzSRgfOlT7JYEyMoB+/UgtajTAY48BK1aY7zeZqC629epQbi7ZlQSefpoizmVl1V+jvJwu\nGlIn+fbtaSlViPJ06gR8/HH1SFFtIK5MwRUpmIZMz550rt+8mcqbSiEljLOySMx27Fh9/6eflq9n\nLBUxBijxb+pUOse9+iqwcCEJ9meekV7R6d+fhPH167TqO24cJdF/9BFNvm11yhSoacS4uJjOZ1FR\n5m1eXvSeyF2fCwvpvQsONm8bMoQmAcXF9F737k2aDCCBbK8w1uloFVBNOVjufGeDEyco81urpUiN\nUsm2NWsoMtS6Nc3KcnOVl/cB+hLv3Uv7XlWRS/Pii+RhHj0aGD9eef+jR2nGJqZRI7PovHqVlnqb\nNjVHjbdvt5xZykWM8/PJ7yQljB2NGHt60o9AqN8qhfhEEhICPPEEzaxtIRbGHTqQH+u99+SPTWD7\ndjqmzZvV7c/cHoTVkLg4Ook5UlZNDhLG5uSLMWNIGAsCPDeXLiLHjlk+zloY9+tHt5cvr/4aBgNV\nZZHKR9BogPffN09qO3Wii4SzhTE392AaMj170tL9X/4C2Mr579qVzjdPPEHX/YMHge+/p2ixVILu\niBF0jbdVKvT48eoRY4CiwhoNrYiOGkXnu++/p9eVQhDGW7ZQQGvmTOCTT4DXXqPJuVLycE2F8dGj\ndF32sspA695dvnyrOFosEBRE1a3+8x/6HOLjzcL4zBnSZ/ag0VjqHDmc3vmuvnLkiNmX27Gj8lJt\nWhrNzgCa+QwZIr98CtAFduZMmgnGxCj7cDZuJAG+cyct9WzYUH0ZV24cAkLE2GSiJZmgIEv/jXXE\nuHVruiB37kyea4H162lbixZUHNyatm3lI+0nT3oiO7u6MAZITMjZKaxn2O3amZdZpLD2Ms+eTTPv\nAttFM6rYuJGsFLm56vZnbg+5ueQB9PMj0VjbPuOtW4G4OHOli+7dSUAKjTcyM+lkqySMAWDWLGDu\n3Or1lo8fl7ZRSBETQxdktfvbg9hjzBFjpiHTuTNdI595xvY+np7AW2+RiE5MpPrHy5ZVt1GI9588\nmSa61phM0lYKgLSHwUDXSG9vYMYMSrALCam+L0DnBo0G+Owzija3a0fX0kcfpdVvJWoqjKX0BkCr\n6HJWEilhDFCexdy5FKFv0YKu8SUldIzNmtl/fGrtFGylsIE9wvjsWRKq4h/F0KHKwnjrVpp1pqQo\n+3hv3KBI8X//S1/eoCCq+fv55/KvIRUxDgigZd3CQpoVeXubZ1LXr1N0XNwdx8ODZsQrVlDUVPjh\nbNwIvPQS2RIaN67+2nIR4y+/BEaMaIyXX5b+QfTta7t3fWkpnSzECUutWsnXsrUWxpGRNANfvNj2\nYwQ2bqQT38CBFEmwh/JyEtW1vczP0PdU+I126lS7PuObN8lq07y5WclqNOaoMUBJcIMGWQrjyko6\neVsn0w0ZQida67qkH35IF1Y1DB9ursJR27DHmGEIrZauFx06yO83cSJZnZ57juwO58/Lt2mfMIGi\nvdesqq9duEDnBltiV5y7M3my/EqnRkNR42++MXfrW7mSLBhqqI2IsbXeAID77qOAmy17pJwwvnyZ\nzrMtWlCkOC+P7K1CMzF7UJuAx8LYBvYI46VLSaSKQ+/9+yt3vPrwQ/pheXkp+3izs73RpIllNvqk\nSbRMUlGhbhwCGg3NiHNzyUYBmL8we/aQyLBeRmjenCJW4vfi0CGaXdtCThh/+CGwaNFlTJsmvbwT\nF0eRcamOZidPmmfQAi1b2hcxBshrvHixfGLl5cs0zvh4Eje//GJ7XynOn/fAJ5+os9Uw9nHkiPni\npWZVR8BgkL+AATRJbNq0+nfzscfoQiM03khKshTGBgNdXMSefoCep18/y1WhvXvpYmErwccaT086\nJmfAHmOGMWPvMvrzz9N1Sa5Ea3AweW+tz1O2osWO0q8fTWz79KHbfn7qReQdd5Bwd7STqK2IcUAA\n2UK+/ZaCRF9/bXndzcmpvsoGkEWzZ0+69rZsScLYkcQ7AXusFCyMJdi3z3zR7dyZxJHUl6WsjBJi\nrP2+7dvTD8VWglxeHkV/hHIqShHjY8e8qpn6Y2PJLywXRbIu1SbQqJGlMBaWGL77zrKbjjWdOlH0\nGKD3pFMn2/s2bkwXW6kktzNngOho24q0SRP6JyV2jh2rnqgg/Ghs/aClhHH37iSwf/jB9hg2byZR\n7ONDP057fcb5+Z5Vx8zULtYRY7VWir17KXIrl9x58SJ9/6zp2JG2//YbWSlGjiSfvlCKScpGIRAT\nY5mAMmcOLY1ai+i6gK0UDFMzFHqQAbCcwP/5J5CY2AizZ0sn3jnKiBEUwXakTr+XF1W8sY5qq8VW\nxBiglbGvv6YiBY8+atm+2lbEWKOh82xEhNlKUVNhrBQxrqigf472OXBbYbxjB0UKhQzO4GASeceP\nV9/3449pFti/v+V2rZY+6CNHpF/j448p2iQsk3TpQl8qW73Cjx3zkhShY8ZQWRYpiovJSyz1JZKK\nGB84QN6k55+Xfj7ALECKishvK9f0Q6OR9hmXltJxKXUH7NePWmsOGEBLSELdSKkMXn9/OjEJX3rr\nKgVSwhggH9n771PG8H33VRfWq1bRdoD8nYWF5gQAW0ydaraBsDB2HocPm4Vxz570+/niC/oMly61\nPYkRLkxyySAXLtiOzo4ZQ1VkgoNpn7ZtzeeGkyfVCeOcHPqOPPWU7BBvG2IrBSffMYxzEAvjTZsA\nna4S995Ltozaon17yltyFEftFCaTvDC+7z4Sw++9R9rn11/N91mXuJQiLIyCbMeOOVcYC9FitV1O\nrXFbYZyaSr4hcWalVES3qIgukG+9Jf081hEigYICWsJ/9lnzNj8/+mLYinodO+YtWQbmrrvoByaF\nkCEqVdYlNJQuzuKI8dKl5HeWSoYTEISxEK1TWqKRslMYDPQlV3rs3LlU/u6NN0jUjhlD0XFbpW0E\nn3FlJXmUhQzgkhKaAUtFAB95hE5UmzbRZyV+/48fJ3ElrAZ4eNCPWy6JQCi+LiRo5ed7SiZo2cIZ\n9XjdEaORyiMJDTFDQuhEO2cOTTJffZUmeFLe7kOHaMVEKIQvhZwwfuwxstTExtLtqCjz5ysXMe7S\nhVZbKivpWO+6y/HM59qGrRQM43zEwnjPHmDYsJuYPp3qFbsKjgrj/Hw6n9nySut0wCuvUAGBlBSz\nMC4rI02gVGXCw4OSrbdtuz3C2FHqnTD+6CP55VOALmy//EJGeTFSwnjhQmrLKFWqDKAL4YEDlttM\nJiq1kpJS3YbQvbttO4WUlQKg1y4ooIioNb/9ZvYZWSNYKYS6gU2a0Jd65kzp/QUEK4WSjUJAShir\nXQoJDydf0p130nGlplI1CSkrBUDC+PRpur+oyDxhEIS41ATB15ce89139Flu2WK+7+23qf6keIns\noYfIJ2WL06epUogQJc/P90TPnuqE8bp19J4KNabV4qgfrD6Tnk7CVPzZtG9PNYXfeosuPiUl0h0o\nDx0icStEjI3G6qUS5YRx69Y08bJXGIeE0ET01CkqBTdokNrROh9rYczJdwxT+1gL4y5dJIqb1zGO\nCmNbtk0xL71E584BAyhH48YNumbq9eqsCy1a0Eqbo8JYTfJdgxLGV65QFFiqjaOY+fNJFFv7haSE\n8bffyi+FSgnjDz6gRKw33qi+f8+e0su7N2+SwJISgx4ewODB0klhP/xgu+aptZVi4EAaj9Dtzhat\nWtF7uW2bOmEs1eRDqnaxGh59lF7711+lhXHLliQ69uwh64vgvd6717LguDXCysCAAeaqAQYDZfZO\nmWK579ChZquNFNu2ka1DLIwHD1YWxjdvUoSzSRPlpE0x69dTzef6ztat0pM7W/z8s7kznJiICKoO\n4+lJSW3/+Y/l/SYTCeO//MX8W3vpJZpwiZETxgBZjv72N/p/VJS55XNurry9KCaGIt3p6fJe/tuN\nj49lVQqOGDNM7dOuHQWGLl0i25Vcnk1d4agwtpV4J0VgIFkTt22jZGaljnwCLVtS0IsjxrXEd99R\nRMRaqIo5e5bsBFOnVr9PEMbC0uyVK/RFsBWRBUgYi60UlZXAvHnkg5TKXrVVouzoUaBFi3KbMyqp\nagnFxfRcQskWa0JDSUQKwjgoSLrFpDVCa9q1a9UJ4+hoshaEhprfV0eFsacnFTovL5eOyglWisxM\nmrDs2UPvw+ef2+5gJGbAAHPE+P33STxZl6ELCKCJyI8/0nvw8MOWy/Xbt1NU2VoYHz8uX7ItNZVE\n0/jx8o1NxJSWkpg+eFDa/16f+PvfKUKvlg0bpIWxmPHj6XMSZyHn5dFnGBdHKy1nzlANcuv378IF\naeuNQKdO5vvVRowB+oz/9z9aPrTlxasLrBt81HbLaYZhKCraujUloXXq5HiClzOpScTYnnPa4MEU\nYHjvPdJFahAEsb3NPQTUCOObN2tmcatXwnjlSoqeChUVpHjzTYoWS0VN9Xr6azDQXyr+L1+epXVr\nuvgKGZ5799JMyVaJs549aZnFumXh4cPyM8u77iJhLBZemzaR0LaVKRsaShdnQRjbQ6dO9OVSI4zv\nvJMM92lpZtF59qzjM77ERBJFUjM6wUqxZw9FwHv1oqLrf/xBXmIloqJotnj4MP1gn3tOer+HHiL/\n86RJ9Fri1qDbt1NiQW4ufR75+R7o1IkicMJ3x5qKClqpeOcdmmipFcb//jd9x5KTzT3l1fDHH8C7\n76rf316WLgXGjlXfiS4/n06qK1fKl84TOHWKIi7du8vvFxJCHSLFUeNDh2g508ODHv/ii1SK8ORJ\ny8cK5drUIAjjCxfIRiP33Y6Joe/knXc6ntzhDMTC+MYNFsYM4yw6dqRzpNDq3dW4HRFjgITxsmXA\n9Onyq2xiWrQg0dqokf3HB1Aw4+JF+SBVg4kYFxaS1/Dll21HjE+epAvzSy9J36/RWNop0tOVPYKe\nnpblzeSsDQB94N26mRO3BA4fBtq1s60YoqLo+MRtq3/4wVxNQQrhi+WoMBaqbijh6UlLJgkJJErK\nyylK50jEWHg+W1Hwli3pc9yzh04699xDP7rHHlPnmdRoKGo8aRL9tVVb8oEHaDln7Vrg//7PXHD9\n5k1aIRgyhD7Lc+eACxc8odebxVN6OrBokeXz7dpFE6927UgY79ih3BCkvJwSP997j4qg//ST8vgE\n5syhGbpc/euasGUL2We6dVO2LgFki7jvPvr8xJnKW7ZQsob1cW7YQJ+tlGfcmunTyb4klFMTe+N7\n9qSqI6+/Tt8b8XuuZKUQ07w5Jc926EAne7koUEwMfU9cyV8MWJZru3HDNUrIMYw70rEjndvcTRjb\nGzGOjwemTQNeeEH9Y1q2JHHsaFDB15fObdY5JWIajDBeu5ZsAr160QVbqiRaWhott0t1cBOwVxgD\nlj5jJWEMmHudizl8GIiKsi2MNRqKGn70Ed02mWgJWe61QkPpryPCuHNnmhla90OXIzCQIvHHjjlu\npROiTNYAACAASURBVFCiVSt6rwMDSdTccw9F6598Uv1zDBxIYk6uZF3jxiSk+ven78z27XRS2LuX\nxJG/P00atm8HgoMrodWafagvvEDWEjFiW0CLFiT45JqVACS6fX3pJCskDdqqmS3m0CHyuIaFVf+e\n1RanTgH/+AdZhh5/nOwscqxfT+I+OZkiKRcvUtR97Fj6Hr/+uuX+tvzFUrRvTysqwm9DLIx79aKI\n++jRdCIUWy7sEcYaDX1ely4pR+47dKDfjSv5iwHLcm0sjBnGeQhJ9K4sjKV6D8hRWkrnQHsalfj5\n0cql3Kq7Nf36UTCqJigl4DUYYfzDD+QF9fWlGYdUItShQ+Ysc1vExlJLxzNnaNlATdJT797AkiUk\nFo4cUe64JSWMDx2SF8YALfunpVF0/IsvKIolN3uriTAeNoyi6/bSrRt1/qqJlUKORo3oMxY+x549\naanGnhPQsGH0XVEqnyPMWP39qQblww/T7LdvX9repg1ZLJo3p3BnVBR1+7t0qbqfVRCGwvOqsVOc\nPWuuyxwSQpFINdHZ99+nShujR8uXnasJp0/T7+z++80VRWxRUUFJkkOHUnLl2rXmxivZ2fTb/fhj\nmjwAFCnfvFmdH15AiOqfOmUpjMeMoc/Iw4MEsmCnMJmUPcaO4uNDn61Su9nbjdhKcf06WykYxll0\n7EiT45iYuj4SaRyJGOfk0DnbHpHrCI0aVW+mZi9KPuMGI4x37zYLFqlKEQBdhJUuVg89RKKrWzfy\nF6t58yZOJBHYvz9FrpS+OP36UaamUIKrpISi3HJWCoCW4keNIivIjBnAf/8r/zo1Ecbe3pAsHadE\nt270WRQUKFe/cASNhgSZIIw9PSnyaM+yS4cOVGfRnsf8858kvMaPN/uS27QhoSoWxn/+SfuJO6UV\nFpLVZsAA8/OpFcbiqPvw4cp2isuXyTowaRLw4IOUkKpk2cjNpfdDLSYTCdBWrej2e+9RdY+sLOn9\nd++mSVxEBH2HX3wR+Oor8lv7+9N9n31GkXaTid6Xli3t+/507UrJmLGxFMUXhLGXl3mC1ro1jRWg\nCLeHh/NKlvXo4Vr+YoCtFAxzu+jalc5prlLD3BpHhLGaUm2uQuPG8m2hG0Ty3blzlMwmZIp37lw9\nAa+yUt0H6+1NiTzvv287McsaT0+KGA8fbm7/LEfz5iRWhfJPWVkkqvz8FBQMSFR88QVF6Lp0kd+3\nJh5jR+nWjaKjzZqp791uL3363P5i6d7eFPF86ilzlL5NG2or3rw5zXB696b2wQ8/bFnbedMmiqqK\nJ1l9+khXJxFjLYzvuUe5XfXvv5M4bNaMoujXr9vuzCiwbh1VE7EW0BMmALm51T/ES5dIcAodHUND\nqazZ559LP//atfTbEPjHP8inK2boUEoU3bVLXTUKKV59lc4FBw9KWyQiI83C2B4bhbvAVgqGuT14\newPjxtX1UdhGEMYmEwV7pk+nldd//pMSvqU4csS1quzIERoqL/wbRMQ4M5O8hEKERipifPo0fRmE\ni7kSf/kLJWCpxcsL+PRTitKpoX9/cwWHnTvV16nt0IFsGHL+WIGAAPqB3m5hfPCgc2wUAl9+6Rr+\nzdat6cQiRIwjI6lOtIcHJdkJdgopoRcfT9FluVmttTCOjSUrQEGB7cfs3m3+Lmk09H201U5cIDub\nIsDi38zBgyR0ly2rvt5++rQ5Wizw+OPkq7b29hcXU+REqR2qRkMn6C++oPfLHhuFGC8v25NfccTY\nnooU7oK1lYKFMcM0TIKDSTieOEGBLJ2OVv2KioDXXpNeZaxPEWOliHiDEMa7d5vbxgLSEWM1Norb\nyT33mD2Vu3bJ10q2pm9fddn6Gg3NWp1habBFZCQlxjkj8c7VECp2CMJYTLt2Zp97RgZVsRDj70+e\n47VrbT+/tTD28iJBLZSOy86uXvZv925LH31CAtl25BB+G2I/cloaiepvvvGvVl7t1KnqNSbbtqWT\nprXV44svaBKjJmFj3DhgxQryCIttJ7WF2GPcUCPGXK6NYRhBOAq5H7Nnk53uvfdIFJ89W/0x9Sli\nzMIY1YVxdDRFtcQZ/K4mjIcNo7rEwvKxszqbffrp7b0AajTkr2oIwrhFC7KL6PXVhXFUFEWMDQbK\n/pX67j36KPmBbSFV2WPwYCp3Vl5OJ7SPPzbfZzKZV08E1HiZs7NpKU0QxpWVVDnijTeAiIiKalUY\npCLGAEWNv/zSfLu8nGo3v/ii/OsLtGhBv4OBA2t20rKFOGLcEIWxuPMdWykYpuEiVKX4+WcK0gnI\nJYZzxNiMKmFcWFiIQYMGITc3F5cuXcIzzzyDlJQUJCcn48yZMwCAVatWYfTo0RgzZgzS09NvHVwJ\npkyZgrFjx2LixIm47EBhPZOpujD29qaIltCZDKByaI4kkzmLZs0o4vjzzxTFUvIL1yfi413rvXYW\nXl4k4lq3rp40KVgpfv+d3g+pCP/w4fTdtZU9KyeMv/mGkvrEnmODgcSoOJrbujWJIFuNR4qK6AQ5\ndiyd+PLzqUxh48b0nXzssevVvMPixDsxiYmUjPj773R74UKqqiEkxarh7bfJ5+YMhOYwlZUNUxhb\nR4xZGDNMw8Tbm5LPfv65es+APn2q91m4epVscUITNFcnNJRyYWzh9OS78vJyzJ49G763XuWdd97B\ngw8+iLS0NDz33HPIyclBQUEB0tLSsHLlSnz66adITU1FWVkZli9fjujoaCxduhQjR47EIuuuCCrI\ny6MLnbWnVdzCFXC9iDFAwuj11ynC6optIx3lrbcoGash8OuvQFhYZbXtgpVi61bbtgA/P2p68ckn\nJNjETS4qK0nMWp+Iunen7/ycOZQgumWLuZOcYKMQV0PQaCgKa32iExCWx3x8aBVjzBhKMBSSSB98\n8AY2bbIsli6UarMmKIgSOEaPpu/1ggVUfcIeunenajDOwM+PIgn5+c4r1ebKCMLYZGJhzDANnZAQ\nsrhZWy2lIsbCdcLVKu3Yos4jxm+99RaSkpLQ9Fb4Zc+ePTh37hyeeOIJrFu3DnFxccjKykJsbCy8\nvLyg0+kQGRmJ7OxsZGZmIuFWeYGEhARsUzJDSiBEi60/MLHHE3BdYbx9u33+YqZ+EBFBP8yff5b3\ny06eTL7a3r0ts5gvXKAECetZrRClBihZrVUr+g0A1W0UAuITnXVShfh3MXs2HcPnnwN//zttCwoy\noV8/qqwhYCtiDFDS3Lx51Ilu/Xrb+9UVgp2iIUaMhXJtN2/S/9XkKTAM456EhFjaKAR696ZrijhQ\nU59sFEAdC+M1a9agUaNGiI+Ph8lkgslkQl5eHoKDg/HFF18gLCwMH3/8MYqLixEYGFj1OH9/fxQX\nF8NoNEKn0wEAAgICUKzUPkuCDRtoqdoaweMJUEj9xg3XWwaIiyPx4yx/MVN3eHiQVSY3V76pTP/+\nVJ1CaHO9YgVtl+sc+NxzJDw9PCipT7BTWFuKBARhXFxMialia4RYGHfoQCXaEhIsS+0NH27Z7c1W\nxFhg/HiKdnfubHufuqJjR2oGsmtXwxPGQrk2jhYzDNO6tXTlrdBQsnqKy3zWp8Q7wPnCWLYh8Jo1\na6DRaPD777/jyJEjmDFjBjw9PTH4VpHSIUOGYP78+YiJibEQvUajEUFBQdDpdDDeSqs3Go0W4lkK\ng5VRsrhYgxUrmmHTpgswGCyXs0NDfXDggA4GQyF27dKibdsg5OfL1LmqI956yxc9epTAYDChqKio\n2hjrO+44JmtsjTEiIgSBgR4oLCxU9Typqd54/PFQtG9/EX/+qUXjxv4wGKobpYTmFQYD0K2bDz77\nTIfRoy9hx46mePXVi9V+Cy1aeGDnzqZ44YXr0Ou9MGuWN0pKrmLkyJvYuzcEI0bcgMEg3Wu6qKgI\nPXtewNtvN0Je3nmUlACXLzdHZWW+Td+yKzNjhgYbN/oiI8MHTZteg8FQ6dbfUfHYiot9ce2aH3Jz\nr8LHpwkMhvN1fHQ1x50/OwF3HqM7j03AVcf4n//QX6lD69IlGBs2lCA4+AYAYP/+EAwdehMGww2L\n/Vx1bKWlHigosH2OKygIQuPGlTAY7A/GAgrC+CuRgXDcuHF49dVXsWDBAqSnp2PkyJHYtWsXoqKi\nEBMTg/nz56O0tBQlJSXIyclBVFQUevTogYyMDMTExCAjIwO9pMJdIvRWId/FiykZKTa2ej2yuDjq\nDqfX65GTQ7etH+8KPPWU+f8Gg8Elj7EmuOOYrLE1xthYsj6oHb9eT0lwK1aEoWVLsgMpPXbUKLJj\n3HtvcyQmAr16hVWzFen1FAX49lsdDh+mJhh33RWKLl0o+tuvn5/N1RSDwYDo6Kbw8QEuX9bDx4ci\n2RER9fczjY4Gnn0WAKhcizt/R8Vja96cVhmCgvwQEOCa50N7cefPTsCdx+jOYxOoj2NMSACOH/eH\nXh8CgFY0+/b1q7ot4Kpja9IEuHYNaN5cL+mL9vamFUO93nZji/z8fJv3yQpjKWbMmIFXXnkFK1as\nQGBgIFJTUxEYGFhVpcJkMmHq1KnQarVISkrCjBkzkJycDK1Wi9TUVNWvYzLRjOedd6Tvb9mSfIQ3\nblCm/KOP2jsShqkZc+bY/5jJk6ll+NixVNFBiaAg6lo3cKC0X0wgMZHaFDduTP+WLAEee4yWm5SW\nyDQaSsxbv57Elav5hhl1iK0UXMOYYRhbdOtmrrFfXk75WvWp0pRQdaOoSLqpm1OtFGKWLFlS9f/P\nJXrDJiYmIjEx0WKbr68vFi5c6NCBZWaSZ/Kuu6Tv9/KiZhMnTlDm/gcfOPQyDOMwWq39j2nXjjzn\nX3xBVSfU8K9/Ke/z9tuWt4cOBaZMoYoYakTS8OHUDTIoyLwEx9QvhKoU7DFmGEaOmBjqhGoykYbS\n6+vfZFrwGTtDGLts3vKWLXRxl8usjooCvvuOWiI3hIYTjHsweTItAzn7OztjhnLzD4GhQ8m6dPy4\nfa3SGddBEMbcDpphGDmaNKGoq8FAnUiFvJb6hFwtY7cVxvv3U81TOdq1owz8O++8PcfEMLXBsGFU\nrcLZWcAaDdCokbp9fXyApKSaFUVn6hah8x1bKRiGUaJLF4oaHzzomhWGlJCrTCGUrHQUlxXG+/Yp\nC+OoKOp+d6tUMsPUCzw8qHucddMahqkJbKVgGEYtMTFUSrS+RozlhHFJiZM739UFpaVUV0+pjXJU\nFP3liDHDMA0dFsYMw6ilvkeMQ0PlhbHbRYwPHaLmCUon9y5dqLlBZORtOSyGYRiXRbBSsMeYYRgl\nYmJoZf7YMdfrGqyGkJAG5jHev5/KiSjRvDmwY0f96e/NMAzjLMQRY/YYMwwjR6dOQFYWEBYGBATU\n9dHYj5KVwu2EsRp/McMwDGOGrRQMw6glMJBW2+ujvxhogMl3LIwZhmHsg8u1MQxjD1261E9/MaDs\nMa5J8p3dne+cjcmk3krBMAzDEOJybY0b1/XRMAzj6kyaRK2T6yPO9Bi7nDA2GKjwdLNmdX0kDMMw\n9QdPT/pbXAy0bFm3x8IwjOtz3311fQSO06A8xoWF9XcGwzAMU5dotcDVq2ylYBjGvWlQwrioiEzh\nDMMwjH34+ABXrrAwZhjGvZHzGN+86WYNPoqKgKCguj4KhmGY+odWS8KYy7UxDOPOBAfT6lhlpeV2\nk4mSkN0qYnztGkeMGYZhHIGtFAzDNAQ8PQGdjs53YsrK6D6PGqhblxPGbKVgGIZxDLZSMAzTUAgO\npvOdmJr6iwEWxgzDMG6DYKVgYcwwjLsTEEB128W4pTC+do09xgzDMI6g1dKFgj3GDMO4O/7+1YVx\nTRPvABcUxhwxZhiGcQytlv5yxJhhGHfH358aGolxy4gxC2OGYRjHEC4ILIwZhnF3pCLGLIwZhmGY\nKjhizDBMQ6FOhXFhYSEGDRqE3Nzcqm3ff/89xowZU3V71apVGD16NMaMGYP09PRbB1iCKVOmYOzY\nsZg4cSIu26rGLII9xgzDMI4hCGP2GDMM4+74+dWRMC4vL8fs2bPhK3IzHzp0CKtXr666XVBQgLS0\nNKxcuRKffvopUlNTUVZWhuXLlyM6OhpLly7FyJEjsWjRIsUD4ogxwzCMY/j4ABqNWSAzDMO4K3WW\nfPfWW28hKSkJTZs2BQBcuXIFCxYswKxZs6r2ycrKQmxsLLy8vKDT6RAZGYns7GxkZmYiISEBAJCQ\nkIBt27YpHhALY4ZhGMfQaimKotHU9ZEwDMM4lzqxUqxZswaNGjVCfHw8TCYTKioqMGvWLLz88svw\nE5nYiouLEShSs/7+/iguLobRaIROpwMABAQEoLi4WPGA2ErBMAzjGIIwZhiGcXecJYy95O5cs2YN\nNBoNfv/9d2RnZ+PBBx9EREQE5syZg5KSEpw4cQJz585FXFycheg1Go0ICgqCTqeD0Wis2haoEAo2\nGAy4erUZjMaLMBgqZfetjxQVFcFgMNT1YdQq7jgma9x5jO48NgF3HqP12CoqguHjo4XBcKEOj6r2\ncOfPTsCdx+jOYxNw5zG6+tjKy3U4f14Dg6Goalt+vi8qK/1gMCjntNlCVhh/9dVXVf9PSUnBa6+9\nhsjISABAXl4epk2bhpkzZ6KgoAALFixAaWkpSkpKkJOTg6ioKPTo0QMZGRmIiYlBRkYGevXqJXsw\ner0eRiMQFRWGW4Fmt8JgMECv19f1YdQq7jgma9x5jO48NgF3HqP12IKDAZ0ObjNed/7sBNx5jO48\nNgF3HqOrjy0sDDAYAL3eHHQNCKDzoF4vv3SWn59v8z5ZYSxGo9HAZDJJ3te4cWOkpKQgOTkZJpMJ\nU6dOhVarRVJSEmbMmIHk5GRotVqkpqbKvkZFBRmnAwLUHhXDMAwjwFYKhmEaCnVipRCzZMkSi9vh\n4eFYsWJF1e3ExEQkJiZa7OPr64uFCxeqPpiiIop2cOIIwzCM/Wi1XKqNYZiGgZQwLi11swYfXJGC\nYRjGcXx8OGLMMEzDQKqOcWlpzctVsjBmGIZxE9hKwTBMQ8GWlcKthPG1ayyMGYZhHIWFMcMwDQVb\nVgq3EsZFRVzDmGEYxlF8fNhjzDBMw4A9xgzDMIwsAQEcXGAYpmHgrIix6qoUtwO2UjAMwzjOuHHk\nsWMYhnF3/P2BGzcst9WGx9ilhDFbKRiGYRzHz489xgzDNAwajMeYI8YMwzAMwzCMHOwxZhiGYRiG\nYRg0kDrG7DFmGIZhGIZhlNBqgYoKoKzMvM3t6hizx5hhGIZhGIZRQqOpnoDndhFjtlIwDMMwDMMw\narD2Gbudx5itFAzDMAzDMIwapIQxR4wZhmEYhmGYBoe1lYI9xgzDMAzDMEyDhCPGDMMwDMMwDAP2\nGDMMwzAMwzAMgOq1jN0uYmw0sjBmGIZhGIZhlLGOGLudx9jPD/D0rOujYBiGYRiGYVwdt/cY33FH\nXR8BwzAMwzAMUx+oM49xYWEhBg0ahNzcXBw+fBhjx47FuHHj8OSTT+LSpUsAgFWrVmH06NEYM2YM\n0tPTAQAlJSWYMmUKxo4di4kTJ+L/27v7sKrr+4/jz4MHRDji3YbK2gWmeHN50WaY2nLMGpWppCYo\noAdTm9rWpZtkYJsxMbxrYF5NmTfNBFFBw9LV7MpKvHQqRrtG02GX4aUFpgI6PUfl9vz+YJyfmCII\neDyH1+Mv/J6796tv58ub9/mc7/fixYsNvo7OSCEiIiIijeGQiXFVVRUJCQl4enpis9lYsmQJr732\nGmlpaTz55JOsX7+ekpIS0tPTyczMZMOGDSQnJ1NZWcnWrVvp27cvGRkZjB07ljVr1jT4WmqMRURE\nRKQxHHIe4+XLlxMVFYWvry8Gg4GVK1fSr18/oLZp9vDwID8/n+DgYIxGIyaTiYCAAAoKCsjLyyMk\nJASAkJAQDh061OBrqTEWERERkca45xPj7OxsunXrxmOPPYbNZgPgBz/4AQBffPEFW7Zs4fnnn8di\nsdDxhtNJeHl5YbFYsFqtmEwmALy9vbFYLA0Wo8ZYRERERBrjxsbYZmuZxtjY0I3Z2dkYDAYOHjxI\nQUEBcXFxpKamcuTIEdauXcu6devo0qULJpOpXtNrtVrx8fHBZDJhtVrt2zre4VxsRuNViosvNS/R\nfezKlSsUFxc7uowW5YqZbubKGV05Wx1XzujK2cD184FrZ3TlbHVcOaMzZCsv9+LCBXeKi/9LZSUY\njT357ruzzXrOBhvjzZs32382m80kJiZy4MABsrKySE9Px+d/I96HHnqIN998k4qKCsrLyyksLCQw\nMJBBgwaRk5NDUFAQOTk5DB48uMFievTwws/Pq1mB7mfFxcX4+fk5uowW5YqZbubKGV05Wx1XzujK\n2cD184FrZ3TlbHVcOaMzZPPzg//8B/z8vLFYaqfFjan57NnbN88NNsY3MhgMVFdXs2TJEvz8/PjN\nb36DwWBgyJAhvPTSS5jNZqKjo7HZbMybNw8PDw+ioqKIi4sjOjoaDw8PkpOTG3wNna5NRERERBrj\nxqUULbGMAprQGKelpQFw5MiRW94eERFBREREvW2enp6sWrWq0cVojbGIiIiINMbNjXFzz2EM99kF\nPtQYi4iIiEhjtMbEWI2xiIiIiDidG89j3BLnMAY1xiIiIiLihDQxFhERERFBa4xFRERERIA2MDHW\n6dpEREREpDE6dID/XUdOa4xFREREpO3y9q6dGLfU5aDhPmuM73DFaBERERERANzdoV272mmxS64x\nNjb6ciMiIiIi0taZTGCxuOjEWERERESkseoaY5dcYywiIiIi0liaGIuIiIiIUL8xdrk1xiIiIiIi\njaWJsYiIiIgIWmMsIiIiIgJoYiwiIiIiAmiNsYiIiIgIoImxiIiIiAigNcYiIiIiIoAmxiIiIiIi\ngIPWGJeWljJixAhOnTrFmTNniI6OZsqUKSxatMh+n6ysLCZMmEBkZCT79u0DoLy8nDlz5jB58mRm\nzZrFxYsXm1+xiIiIiAgOmBhXVVWRkJCAp6cnAEuXLmXevHls3ryZmpoa9u7dS0lJCenp6WRmZrJh\nwwaSk5OprKxk69at9O3bl4yMDMaOHcuaNWuaX7GIiIiICLWNsdV6D9cYL1++nKioKHx9fbHZbBw/\nfpzBgwcDEBISwj/+8Q/y8/MJDg7GaDRiMpkICAigoKCAvLw8QkJC7Pc9dOhQ8ysWEREREeEeT4yz\ns7Pp1q0bjz32GDabDYCamhr77d7e3lgsFqxWKx07drRv9/Lysm83mUz17isiIiIi0hJaeo2xsaEb\ns7OzMRgMHDx4kBMnThAXF1dvnbDVasXHxweTyVSv6b1xu9VqtW+7sXm+leLi4uZkue9duXLF5TK6\nYqabuXJGV85Wx5UzunI2cP184NoZXTlbHVfO6CzZrl41culSF7y9q7lyxUpxcXmznq/Bxnjz5s32\nn2NiYli0aBErVqzg6NGjPPLII+zfv59hw4YRFBTEypUrqaiooLy8nMLCQgIDAxk0aBA5OTkEBQWR\nk5NjX4JxO35+fs0Kc78rLi52uYyumOlmrpzRlbPVceWMrpwNXD8fuHZGV85Wx5UzOku28nK4fh0M\nBnd69vSkMSWfPXv2trc12BjfSlxcHAsXLqSyspLevXszcuRIDAYDZrOZ6OhobDYb8+bNw8PDg6io\nKOLi4oiOjsbDw4Pk5OSmvpyIiIiIyC219BrjRjfGaWlp9p/T09O/d3tERAQRERH1tnl6erJq1apm\nlCciIiIicmsOOY+xiIiIiMj9xtOztim+elVXvhMRERGRNsxgqJ0al5WpMRYRERGRNs7bW42xiIiI\niAgmU+1SCq0xFhEREZE27X/XktPEWERERETaNjXGIiIiIiKoMRYRERERAf6/MdYaYxERERFp0+oa\nY3f35j+XGmMRERERcVomExiN4NYCXa0aYxERERFxWiZTy6wvBjXGIiIiIuLETKaWWV8MaoxFRERE\nxIlpYiwiIiIighpjERERERFAjbGIiIiICKA1xiIiIiIigCbGIiIiIiIADBgAU6a0zHOpMRYRERER\np/XDH0JsbMs8l/FOd6ipqeEPf/gDp06dws3NjUWLFlFVVUVCQgJGo5GAgACSkpIAyMrKIjMzE3d3\nd2bPns2IESMoLy9n/vz5lJaWYjKZWLZsGV26dGmZ6kVEREREWsgdJ8affvopBoOBrVu3MnfuXFJS\nUli9ejUvvfQSGRkZlJeXs2/fPkpKSkhPTyczM5MNGzaQnJxMZWUlW7dupW/fvmRkZDB27FjWrFlz\nL3KJiIiIiDTJHRvj0NBQFi9eDEBRURGdOnViwIABXLx4EZvNhtVqxWg0kp+fT3BwMEajEZPJREBA\nAAUFBeTl5RESEgJASEgIhw4dat1EIiIiIiJ3oVFrjN3c3IiPjycpKYmwsDD8/f1JSkpi9OjRlJWV\nMWTIECwWCx07drQ/xsvLC4vFgtVqxWQyAeDt7Y3FYmmdJCIiIiIizXDHNcZ1li1bRmlpKeHh4ZSX\nl7NlyxZ69+5NRkYGy5Yt4+c//3m9ptdqteLj44PJZMJqtdq33dg83ywvL68ZUZzD2bNnHV1Ci3PF\nTDdz5YyunK2OK2d05Wzg+vnAtTO6crY6rpzRlbPdzh0b4/fff59z584xc+ZM2rdvj5ubG507d8bb\n2xuA7t27889//pOgoCBWrlxJRUUF5eXlFBYWEhgYyKBBg8jJySEoKIicnBwGDx58y9cJDg5u2WQi\nIiIiIk1gsNlstobucO3aNRYsWEBJSQlVVVXMnDmTzp0788Ybb2A0GvHw8GDx4sX4+fmxfft2MjMz\nsdlsvPjii4SGhnL9+nXi4uK4cOECHh4eJCcn061bt3uVT0RERESkUe7YGIuIiIiItAW6wIeIiIiI\nCA5qjM1mM6dOnXLES7eqoqIigoODiYmJwWw2ExMTc9vzNjvLf4Pc3Fz69+/Phx9+WG97WFgYCxYs\ncFBVrWf9+vUMHz6ciooKR5fSbG1t34HzvK/uVkP5nnjiCaf9/9aV3ne3sm7dOqZNm4bZbGbqWAVH\ngwAADBJJREFU1KkcO3bM0SW1qG+//ZY5c+YQExNDdHQ0iYmJ9i/d3+zs2bN89tln97jCu5ebm8vg\nwYM5d+6cfVtycjLvvfeeA6tqGbm5ufzsZz+z9yxRUVH8/e9/d3RZDtfos1JI4wQGBpKWluboMlrU\ngw8+yIcffsioUaMA+Oqrr7h+/bqDq2odu3fvZsyYMXzwwQeMHz/e0eU0W1vad22dwWBwdAl3zdXe\ndzf6+uuv+fTTT9m2bRsABQUFxMfHu0RjBVBeXs6LL77IkiVLCAoKAuC9994jNjaWv/zlL9+7/+HD\nhyksLOTxxx+/16XeNQ8PDxYsWMBf//pXR5fS4h599FGSk5MBuHr1KlOmTKFXr17079/fwZU5jsOW\nUpSVlTF79mxmzJhBWFgYn3zyCQDPPvssr7/+un3i6mznPb7Vku2UlBQmT55MZGQkH330kX37qlWr\nmDp1KjNnzuTixYv3sswm6d+/P8XFxfZ9sWvXLp599lkAMjIymDp1KpMmTWL27NlUVVWxc+dOpkyZ\nwuTJkzl8+LAjS2+S3Nxc/P39iYyMZMuWLUDthC4hIQGz2YzZbKa0tJTc3FwmTpzIlClT2LVrl4Or\nblhT9l1lZSWxsbHk5OQAtb/QZ82a5bDa79Zbb71FZmYmAIWFhZjNZsD5jy11bpfPWb8ucrv3Xd1k\nfNu2bfz5z38GYPXq1Tz33HPMmDGDyZMnc/ToUYfV3Vgmk4nvvvuOHTt2cO7cOfr378/27dv56quv\niImJISYmhjlz5mCxWMjNzWX69OnMmDGDcePGkZGR4ejy72jfvn0MHTrU3hQDjBs3jkuXLnH69GnM\nZjORkZFMmzaN0tJS1q1bxwcffOBUU+Nhw4bRqVOn7+2PjRs3Eh4eTmRkpL25nDBhAsXFxQB89NFH\nLFmy5J7Xe7e8vLyIiopiz549pKSkEB0dXa9v+de//kVkZCSTJk1izpw5LvsJj8Ma44KCAmbMmMHb\nb79NYmKi/YBosVgICwsjPT0dX19f9u/f76gS78rJkyfrLaXYvXs33377LRkZGaSlpZGamsqVK1cA\nePrpp9m0aRMjRoxg7dq1Dq68YU899RQff/wxAPn5+QwaNIiamhouXbrEpk2byMzMpLKyki+//BLA\nfhAZNmyYI8tuku3btxMeHk5AQADu7u7k5+cDtacSTE9PZ9SoUaSmpgJQUVHB5s2b7U3m/ayx++7f\n//43kyZNYufOnQC8++67REREOLL0u3Lz5LTu385+bKlzu3zO6lbvu1tlKigo4MCBA2RnZ7NmzRpK\nSkocUG3Tde/endTUVL744gsiIyMZNWoUn332GQsXLiQhIYG0tDRCQkJYv349AOfPn2ft2rVkZmay\nadMmysrKHJygYd988w0//vGPv7f9Rz/6ERMmTGD27Nls27aNmJgYTpw4waxZsxgzZoxTTYwNBgN/\n/OMf2bRpE2fOnAFqjyd79uwhKyuLbdu2cfr0afbt20dERIT9GJqdnc3EiRMdWXqTde3alT179lBU\nVMSWLVvq9S0JCQksXbqUzMxMfvGLX/D11187utxWcc+WUly9epX27dvTrl07oLbZWL9+PTt27ACg\nsrLSft8BAwYA0LNnT6f7i+TmpRQbNmzg2LFjxMTEYLPZqK6upqioCMB+TueHH374vv4lbTAYGDNm\nDAkJCTzwwAM88sgj2Gw23NzccHd3Z968eXTo0IHz589TVVUFQK9evRxcddNcvnyZ/fv3U1ZWRnp6\nOhaLhc2bN2MwGBg6dCgAgwYNsn+y4Sz5mrrvhgwZwuLFiykrK+PgwYPExsY6OsId3XxsudHNU1Rn\nPLY0JZ+zud377kZ1GQsLC3nooYcAaN++PQMHDrzn9d6NM2fO4O3tbZ8cHjt2jBdeeIGKigoWLVoE\nQFVVFf7+/kDtccZoNGI0GgkMDOSbb76ha9euDqv/Trp3724fItzo9OnTlJeX85Of/ATA3gjXNY3O\nplOnTixYsIC4uDiCg4Pt2dzcaueLDz/8MCdPniQyMpLo6GgiIiKwWq306dPHwZU3TXFxMWFhYeza\ntet7fUtJSYn9d9+ECRMcXGnruWcT4/j4ePLy8qipqaGsrIxly5Yxbtw4li9fztChQ53+AF/n5hwP\nPvggQ4cOJS0tjbS0NEaOHGn/67ruYPL5558TGBh4z2ttigceeIBr166Rnp5un5JaLBY++eQTUlJS\nWLhwIdXV1fb8dQcLZ/H+++8THh7O22+/zYYNG8jKyuLgwYNcvHjR/kWZvLw8+35ypnxN3Xdjx44l\nKSmJ4cOH37IZu9/cfGzp168f58+fB3CJLzm5cr7bve/atWtnz3j8+HEA+vTpY/9EqqKiwr79fnfi\nxAkSExPtwx9/f398fHzw9/dnxYoVpKWl8fLLL9sbx+PHj2Oz2bh27RonT560N8z3q1/+8pccOnTI\nvm+g9lOArl27MmLECPv23bt3k5GRgcFgoLq62lHlNsvjjz9Or169yM7Opn379uTn51NTU4PNZuPz\nzz8nICAAk8nEwIEDWbp0Kc8995yjS76jG3sWi8VCVlYWPj4+t+xbfH197RPz9evXs3fvXkeV3aru\n2cR4+vTpLF68GIPBwMiRI+nduzfLly9n3bp1+Pr6cunSJaD+x4LO+BHhzTU/8cQT5ObmMnnyZK5d\nu0ZoaCje3t4YDAb27t3LO++8Q8eOHVm+fLmDKm68UaNGsWvXLvz9/Tlz5gxGo5EOHToQFRUFgK+v\nr/2XmbN59913WbFihf3fnp6ePPXUU+zYsYOdO3eyceNGvLy8WLFiBSdOnHBgpXenKftu/PjxvPnm\nm/ztb39zZMmNduOx5ZlnnmH06NHMnTuXo0eP1psqOuux5W7yOYtbve+efvppevToQWJiIj179qR7\n9+4A9O3bl5CQECZOnEiXLl1wd3fHaLz/vz/+5JNPUlhYSHh4ON7e3tTU1PDKK6/Qs2dP5s+fT3V1\nNW5ubiQlJXHu3Dmqqqp44YUXuHTpEr/+9a/p3LmzoyM0yMvLi9TUVJYsWcJ///tfqqur6devHykp\nKZSVlfHaa6+RmppKhw4deOONNygqKmLt2rUMHDjQ/qVgZ/Lqq69y+PBhTCYTI0eOJDIyEpvNRnBw\nMKGhoQBMnDiRX/3qVyxdutTB1d7ZkSNHiImJwc3NjerqaubOnUtoaCjLli37Xt+yaNEiFixYgJub\nG76+vjz//POOLr9V6AIfIg0wm80kJiY6zdKJlnDu3Dni4+PZuHGjo0sRsSsrK2PPnj1ER0dTUVFB\nWFgYmzZtokePHo4urcXk5uaSmZlp/yKXiNx79/+f2yIO5IxTuOb4+OOPeeutt+xrH0XuF126dOHL\nL78kPDwcNzc3IiIiXKopFpH7gybGIiIiIiK08sS4qqqKV199laKiIiorK5k9ezZ9+vQhPj4eNzc3\nAgMDSUhIsN+/rKyMqKgodu/ejYeHBxaLhZdffhmr1UplZSXx8fH89Kc/bc2SRURERKSNatXGeNeu\nXXTp0oUVK1Zw+fJlxo4dS//+/Zk3bx6DBw8mISGBvXv3EhoayoEDB0hOTqa0tNT++I0bN9ovV3jq\n1CliY2PJzs5uzZJFREREpI1q1XNOPfPMM8ydOxeA6upq2rVrx/Hjx+3n7w0JCeHQoUMAtGvXjnfe\neYdOnTrZHz9t2jQiIyOB2ulz+/btW7NcEREREWnDWrUx7tChA15eXlgsFubOncvvfve7eufM8/b2\ntl8F7tFHH6VTp071bjeZTHh4eHDhwgVeeeUVp7jYgIiIiIg4p1a/SsHZs2eZOnUq48ePZ/To0fUu\njGC1WvHx8al3/5vPAnDixAmmT59ObGysfdIsIiIiItLSWrUxLikpYcaMGcyfP5/x48cDtZdkPXr0\nKAD79+8nODi43mNunBifPHmS3/72t/zpT39i+PDhrVmqiIiIiLRxrfrlu7Vr13L58mXWrFnD6tWr\nMRgM/P73v+f111+nsrKS3r17M3LkyHqPuXFinJKSQkVFBUlJSdhsNnx8fFi9enVrliwiIiIibZTO\nYywiIiIiwj1YYywiIiIi4gzUGIuIiIiIoMZYRERERARQYywiIiIiAqgxFhEREREB1BiLiIiIiABq\njEVEREREADXGIiIiIiIA/B/rXnxzXnFSGQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1144abdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 4))\n",
    "births_by_date.plot(ax=ax);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we're communicating data like this, it is often useful to annotate certain features of the plot to draw the reader's attention.\n",
    "This can be done manually with the ``plt.text``/``ax.text`` command, which will place text at a particular x/y value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GIcaRI0DXrkCvXsD+/brXvXGDBJ9Qbq1pU5p4wZDjpKZSZDs4mDzYq1eT3YOm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\n+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      "text/plain": [
       "<matplotlib.figure.Figure at 0x114c827b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 4))\n",
    "births_by_date.plot(ax=ax)\n",
    "\n",
    "# Add labels to the plot\n",
    "style = dict(size=10, color='gray')\n",
    "\n",
    "ax.text('2012-1-1', 3950, \"New Year's Day\", **style)\n",
    "ax.text('2012-7-4', 4250, \"Independence Day\", ha='center', **style)\n",
    "ax.text('2012-9-4', 4850, \"Labor Day\", ha='center', **style)\n",
    "ax.text('2012-10-31', 4600, \"Halloween\", ha='right', **style)\n",
    "ax.text('2012-11-25', 4450, \"Thanksgiving\", ha='center', **style)\n",
    "ax.text('2012-12-25', 3850, \"Christmas \", ha='right', **style)\n",
    "\n",
    "# Label the axes\n",
    "ax.set(title='USA births by day of year (1969-1988)',\n",
    "       ylabel='average daily births')\n",
    "\n",
    "# Format the x axis with centered month labels\n",
    "ax.xaxis.set_major_locator(mpl.dates.MonthLocator())\n",
    "ax.xaxis.set_minor_locator(mpl.dates.MonthLocator(bymonthday=15))\n",
    "ax.xaxis.set_major_formatter(plt.NullFormatter())\n",
    "ax.xaxis.set_minor_formatter(mpl.dates.DateFormatter('%h'));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ``ax.text`` method takes an x position, a y position, a string, and then optional keywords specifying the color, size, style, alignment, and other properties of the text.\n",
    "Here we used ``ha='right'`` and ``ha='center'``, where ``ha`` is short for *horizonal alignment*.\n",
    "See the docstring of ``plt.text()`` and of ``mpl.text.Text()`` for more information on available options."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transforms and Text Position\n",
    "\n",
    "In the previous example, we have anchored our text annotations to data locations. Sometimes it's preferable to anchor the text to a position on the axes or figure, independent of the data. In Matplotlib, this is done by modifying the *transform*.\n",
    "\n",
    "Any graphics display framework needs some scheme for translating between coordinate systems.\n",
    "For example, a data point at $(x, y) = (1, 1)$ needs to somehow be represented at a certain location on the figure, which in turn needs to be represented in pixels on the screen.\n",
    "Mathematically, such coordinate transformations are relatively straightforward, and Matplotlib has a well-developed set of tools that it uses internally to perform them (these tools can be explored in the ``matplotlib.transforms`` submodule).\n",
    "\n",
    "The average user rarely needs to worry about the details of these transforms, but it is helpful knowledge to have when considering the placement of text on a figure. There are three pre-defined transforms that can be useful in this situation:\n",
    "\n",
    "- ``ax.transData``: Transform associated with data coordinates\n",
    "- ``ax.transAxes``: Transform associated with the axes (in units of axes dimensions)\n",
    "- ``fig.transFigure``: Transform associated with the figure (in units of figure dimensions)\n",
    "\n",
    "Here let's look at an example of drawing text at various locations using these transforms:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1144abd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(facecolor='lightgray')\n",
    "ax.axis([0, 10, 0, 10])\n",
    "\n",
    "# transform=ax.transData is the default, but we'll specify it anyway\n",
    "ax.text(1, 5, \". Data: (1, 5)\", transform=ax.transData)\n",
    "ax.text(0.5, 0.1, \". Axes: (0.5, 0.1)\", transform=ax.transAxes)\n",
    "ax.text(0.2, 0.2, \". Figure: (0.2, 0.2)\", transform=fig.transFigure);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that by default, the text is aligned above and to the left of the specified coordinates: here the \".\" at the beginning of each string will approximately mark the given coordinate location.\n",
    "\n",
    "The ``transData`` coordinates give the usual data coordinates associated with the x- and y-axis labels.\n",
    "The ``transAxes`` coordinates give the location from the bottom-left corner of the axes (here the white box), as a fraction of the axes size.\n",
    "The ``transFigure`` coordinates are similar, but specify the position from the bottom-left of the figure (here the gray box), as a fraction of the figure size.\n",
    "\n",
    "Notice now that if we change the axes limits, it is only the ``transData`` coordinates that will be affected, while the others remain stationary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1144abd30>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.set_xlim(0, 2)\n",
    "ax.set_ylim(-6, 6)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This behavior can be seen more clearly by changing the axes limits interactively: if you are executing this code in a notebook, you can make that happen by changing ``%matplotlib inline`` to ``%matplotlib notebook`` and using each plot's menu to interact with the plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Arrows and Annotation\n",
    "\n",
    "Along with tick marks and text, another useful annotation mark is the simple arrow.\n",
    "\n",
    "Drawing arrows in Matplotlib is often much harder than you'd bargain for.\n",
    "While there is a ``plt.arrow()`` function available, I wouldn't suggest using it: the arrows it creates are SVG objects that will be subject to the varying aspect ratio of your plots, and the result is rarely what the user intended.\n",
    "Instead, I'd suggest using the ``plt.annotate()`` function.\n",
    "This function creates some text and an arrow, and the arrows can be very flexibly specified.\n",
    "\n",
    "Here we'll use ``annotate`` with several of its options:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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XvLg+CSYnwtyXn59u+Nx7r9WVeC+f7yMXQrjW+++/T1aW5y/L6E4kyIUQLrNr\n1y7Gjh3LvHnzrC7Fq0iQCyFcQinFyJEj8fPzY9q0aTjz/JyvkSAXQrjE2rVrSUpK4p577iEzM5Pv\nPXWBXTckQS6EcIkxY8bw+uuvU758ed58802ioqKsLslryKgVIYRLjBs3jsqVKxMaGkr//v25dOmS\n1SV5DWmRCyFc4oknnuDXX3/lgQceICAggGHDhlldkteQIBdCuExcXBz169e3ugyvI0EuhHAZCXLn\nkCAXQrhETk4OBw4coF69elaX4nUkyIUQLnHixAnKlStHeZl20uHsGrViGEZZ4FugLFACGGGa5h5H\nFiaE8C7SreI89rbI3wC+N02zLTAI+NxhFQkhvJIEufPYG+STgRnXvi4BpDumHCGEt/r3v/8tQe4k\nd+1aMQxjMPA6oAC/a58HmaYZaxhGFWAeIANChRD5unz5Mps2beKrr76yuhSvdNcgN01zDjDn9tsN\nw6gHLED3j+/I7/dtNluRChRaSkqK7EsHkv3pWHfbn9999x1NmjQhIyND9rsT2Huysy6wBOhrmuaB\nO91XVmFxDFnRxrFkfzrW3fZndHQ0gwYNkn1eQAkJCYW6v71zrbwPBAKfGYbhB1w0TfNxO7clhPBi\nSUlJbN++nYULF1pditeyK8hN0+zl6EKEEN5p+fLlPPLII5QpU8bqUryWXBAkhHCqhQsX0r9/f6vL\n8GoS5EIIpzl9+jT79++na9euVpfi1STIhRBOM3v2bHr37k3JkiWtLsWrycISQginOHv2LFOmTOGn\nn36yuhSvJy1yIYRTTJw4kaeeeoqwsDCrS/F60iIXQjjcsWPHmDdvHocOHbK6FJ8gLXIhhMONGzeO\nYcOGUalSJatL8QnSIhdCONS+ffv4/vvv+eKLL6wuxWdIi1wI4VCjR4/m7bfflguAXEiCXAjhMNHR\n0Rw+fJgXXnjB6lJ8inStCCEcwmazMWjQIObPn09AQIDV5fgUaZELIYosOzubJ598kpdffpn27dtb\nXY7PkSAXQhTZRx99RFBQEGPHjrW6FJ8kXStCiCJZv349y5cvZ//+/RQrJm1DK0iQCyHsdvLkSQYN\nGsSMGTMIDQ21uhyfJW+fQgi7ZGZm0q9fP/7yl7/QrFkzq8vxaRLkQohCy8nJ4U9/+hMVK1ZkxIgR\nVpfj86RrRQhRKFevXuX555/n2LFjrF27VvrF3YAEuRCiwK5evcrgwYM5efIk69ato1SpUlaXJJAg\nF0IU0NUzR1DNAAAMnElEQVSrV3nuueew2WysW7eO4OBgq0sS10iQCyHuKjs7m2effZbExETWrFkj\nIe5mJMiFEHeUnZ3NwIEDOX/+PKtXryYoKMjqksRt5CyFECJfqampPPnkk1y8eJFVq1ZJiLspCXIh\nRJ5M06R58+YEBwezYsUKWUDZjUmQCyFyWbJkCS1btuS1115j7ty5EuJurkh95IZh3AfsASqZppnp\nmJKEEFbJzMzkzTffZO3atWzcuJHGjRtbXZIoALuD3DCMMsDHwBXHlSOEsMrJkyfp27cvlStXJiYm\nhpCQEKtLEgVUlK6VmcBoIM1BtQghLLJx40aaNWtG7969WblypYS4h7lri9wwjMHA64C66eaTwELT\nNA8YhuHnrOKEEM6VkpLChAkTWLRoEYsXL6ZNmzZWlyTs4KeUuvu9bmMYxmHgFOAHPAj8aJpm29vv\nFxsbq6pWrVrUGgX6H04Ws3UcX9+fSilWr17Nu+++S6tWrXj77bepWLGi3dvz9f3paAkJCURGRha4\nkWxXH7lpmhHXvzYM4xjQKb/7VqtWzZ6HELex2WyyLx3Il/fnb7/9xquvvkpiYiJLly6lZcuWRd6m\nL+9PZ0hISCjU/R0x/FChW+ZCCDeWmprK6NGjadmyJd27dyc2NtYhIS6sV+RL9E3TDHNEIUII51BK\nsXLlSoYPH07Lli2Ji4uT1rOXkblWhPBiBw4c4K233uL48eN8/fXXtGvXzuqShBPIlZ1CeKGffvqJ\nnj170rlzZzp27Mi+ffskxL2YBLkQXkIpxdatW+ncuTN9+vShU6dOxMfH88YbbxAQEGB1ecKJpGtF\nCA+nlCI6OpqJEydy5swZRo0axcCBAyW8fYgEuRAeKicnh5UrVzJx4kQyMjIYO3YsTzzxBP7+8m/t\na+QZF8LDXLp0iYULFzJ16lSCgoJ455136NGjhyyC7MMkyIXwANf7v2fPns2aNWvo2LEjn376KR07\ndsTPTy7j8HUS5EK4sdOnTzN37lzmzJlDyZIlGTJkCJMnTyY0NNTq0oQbkSAXws1kZmaydu1aZs+e\nze7du3niiSeYP38+zZo1k9a3yJMEuRBuQCnF/v37+fbbb5k3bx733XcfgwcPZsmSJZQqVcrq8oSb\nkyAXwiJZWVls27aNVatWsXr1aooXL07fvn3ZsWMH4eHhVpcnPIgEuRAulJyczIYNG1i9ejUbNmwg\nPDycHj16sHbtWu6//37pOhF2kSAXwslOnTrF6tWrWbVqFbt376Zly5b07NmTjz76SCavEg4hQS6E\ng2VkZPDzzz+zadMmVq1axfHjx+nWrRsvvPACy5YtkwUYhMNJkAtRRGlpaezZs4dt27axdetWfv75\nZ+677z7atm3LpEmTaNmypVxtKZxKXl1CFFJKSgq7du1i69atbNu2jX379lG/fn1at27NyJEjefjh\nhylbtqzVZQofIkEuxF1cuHCBnTt33gjuX3/9lcjISNq0acOECRNo0aKFDBEUlpIgF+IapRQ2m429\ne/fe8nHu3DmaN29OmzZtiIqKonnz5pQsWdLqcoW4QYJc+KScnBwOHz6cK7QBGjVqRKNGjejXrx8f\nfPAB9957r0xIJdyaBLnwepcuXeLIkSPExcXdCOz9+/dTsWLFG6H96quv0qhRI6pVqyZjuYXHkSAX\nXiEtLY2jR49y5MgRDh8+fMvn1NRUwsPDeeCBB2jUqBG9e/emcuXK1K1b1+qyhXAICXLhMTIzMzl2\n7FiuoD58+DDnzp0jLCyM8PBwIiIiaNGiBc8++yzh4eFUrVo1VyvbZrNZ9FcI4XgS5MItZGdnk5CQ\nwOnTpzl16tSNz9e//v3330lISKBGjRpEREQQHh5OvXr16N27NxEREdSsWZPixYtb/WcIYQmvD/IV\nK1YQHx/PiBEjiryt9u3bEx0dXai1EFesWME999yT7wrmM2fOpEWLFtSrV6/I9bmr9PT0fAP6+uez\nZ88SGhpK9erVqVGjBjVq1KB69eo0bNjwxm21atWSdSiFyIPXBzngsJNX9mzn8ccfv+PPhw4dam85\nLpeTk8OlS5dISkrK9+P8+fO5bsvKyroRxtc/33vvvbRp0+ZGaFepUkWufhTCTj71nzNnzhzWr1+P\nv78/TZs2ZcSIEZw/f55Ro0aRnJwMQFRUFIGBgYwfP56srCwSExMZPnw4HTp0QCmVa5uPPfYYTZs2\nxTRNwsLCqFChAjExMQQGBjJjxgy+/PJLQkNDqV27NrNmzaJEiRKcOnWK7t278+KLLzJ69Gi6d+/O\n2bNn2bx5M1euXOHcuXMMHDiQH374gSNHjvDWW29x33330bJlS3bs2AHAG2+8Qf/+/Tl16tQdf699\n+/a31Judnc3hw4dJSUkhOTmZlJSUfD9uD+ULFy5QqlQpKlSokOdH3bp187y9dOnSMhJECCeyK8gN\nwygGTAYigUBggmma6x1ZmKMdPnyYjRs3smTJEooVK8awYcPYsmULO3fupEOHDvTr1499+/YRFxdH\nhQoVGDJkCE2bNmXv3r1MmzaNDh065Lnd1NRUevToQcOGDenatStjxoxh+PDhDBw4kKNHj95y34SE\nBNasWcOVK1do1aoVL774Yq5tzZ49m/Xr1zN37lwWL17Mjz/+yLx58xgzZky+f1t+v/fNN9/kCvLN\nmzczbNgwypQpc+OjbNmyt3xftWpVypQpQ0hIyC2BHBISQokSJex8BoQQzmJvi3wg4G+aZivDMKoB\nfRxYk1PEx8fToEGDGxd2NG7cmCNHjnD8+HH69NHlN2zYkIYNG3L06FGmT5/OsmXLAL0AQH78/Pxu\nDGMrW7YsderUufF1ZmbmLfeNiIjAz8+PoKCgPK8MvL6dMmXKEBYWBkC5cuXIyMjIdd+bjw7y+73b\nHx+gU6dOHDp0KN+/Rwjheey9XO0RwGYYxlpgJrDGcSU5R1hYGHFxceTk5KCUIiYmhtq1a1OnTh3i\n4uIAiImJ4eOPP+azzz6jV69efPjhhzRv3jzPLpXr7vSzwrpb90N2djbp6elkZmbe0tqXbgshfNtd\nW+SGYQwGXgduTqyzQLppmo8ahtEa+Bpo45QKHSQiIoIuXbrw5JNPopQiMjKSjh070rhxY8aMGcPq\n1aspVqwYEydOZP/+/Xz44YfMnDmTSpUqcfHiRSDvwLz5tvy+vtNthfHMM8/Qt29fatasSfXq1Yu0\nLSGE9/Czp0VpGMZCYIlpmiuufZ9gmmbV2+8XGxurqlbNdbOwQ0pKiixI4ECyPx1L9qdjJSQkEBkZ\nWeCWn7195DuAbsAKwzAaACfyu6MsZeUYNptN9qUDyf50LNmfjpWQkFCo+9sb5LOA6YZh7L72/Z/s\n3I4QQogisivITdPMBIY4uBYhhBB2kEmWhRDCw0mQCyGEh5MgF0IIDydBLoQQHk6CXAghPJwEuRBC\neDgJciGE8HB2XaJfULGxsc7buBBCeLHCXKLv1CAXQgjhfNK1IoQQHk6CXAghPJzD1+w0DMMP+AJo\nAFwBnjdNM97Rj+NLDMOIBS5d+/aYaZoyz40dDMNoDnxgmmY7wzDqoOfRzwF+MU3zFUuL8zC37cuG\nwFrg8LUfTzdNc6l11XkOwzD8gTnAH4EAYCJwkEK+Np3RIu8FBJqm+RAwGr22p7CTYRiBAKZptr/2\nISFuB8Mw3kTP2hl47abJwBjTNNsAxQzD6GlZcR4mj30ZCUy66TUqIV5wA4Bzpmm2BroA07DjtemM\nIG8JRAOYpvkj0MQJj+FLGgClDMPYaBjG99daQqLwjgKP3/R9pGma2699vQHo6PqSPFaufQl0Nwxj\nq2EYXxmGUcqiujzREuCda18XB7KBxoV9bTojyMvyv24AgGzDMKQv3n5pwEemaT4CvATMl/1ZeNdW\ns8q+6aabh3alAOVcW5HnymNf/gi8ea0FGQ9MsKIuT2SaZpppmqmGYZQBlgJjseO16YxASAZuXvOp\nmGmaOU54HF9xGJgPYJrmESAJkPXziu7m12QZ4KJVhXiBlaZp7r329QqgoZXFeBrDMGoCm4C5pmku\nwo7XpjOCfCd6GTgMw3gQOOCEx/Alg4FJAIZhVEM/sYVbB0rk5d/XFg4H6Apsv9OdxR1tNAzjehdq\nByDWymI8iWEYlYGNwEjTNOdeu3lvYV+bDh+1gn5H7mQYxs5r3w9ywmP4ktnAPwzD2I5+px4sRzgO\n8RdglmEYJYBDwDKL6/FkLwFTDcPIBP4LDLW4Hk8yGrgHeMcwjHGAAl5D788Cvzblyk4hhPBwctJM\nCCE8nAS5EEJ4OAlyIYTwcBLkQgjh4STIhRDCw0mQCyGEh5MgF0IIDydBLoQQHu7/A4OHYIwYoOhe\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114622cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "x = np.linspace(0, 20, 1000)\n",
    "ax.plot(x, np.cos(x))\n",
    "ax.axis('equal')\n",
    "\n",
    "ax.annotate('local maximum', xy=(6.28, 1), xytext=(10, 4),\n",
    "            arrowprops=dict(facecolor='black', shrink=0.05))\n",
    "\n",
    "ax.annotate('local minimum', xy=(5 * np.pi, -1), xytext=(2, -6),\n",
    "            arrowprops=dict(arrowstyle=\"->\",\n",
    "                            connectionstyle=\"angle3,angleA=0,angleB=-90\"));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The arrow style is controlled through the ``arrowprops`` dictionary, which has numerous options available.\n",
    "These options are fairly well-documented in Matplotlib's online documentation, so rather than repeating them here it is probably more useful to quickly show some of the possibilities.\n",
    "Let's demonstrate several of the possible options using the birthrate plot from before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UgRkI573atdOIjpaUa+5JhKG44AI5P/9c0rnJlVdKZPXkSflp0yb0NrfeKvmv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117960208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 4))\n",
    "births_by_date.plot(ax=ax)\n",
    "\n",
    "# Add labels to the plot\n",
    "ax.annotate(\"New Year's Day\", xy=('2012-1-1', 4100),  xycoords='data',\n",
    "            xytext=(50, -30), textcoords='offset points',\n",
    "            arrowprops=dict(arrowstyle=\"->\",\n",
    "                            connectionstyle=\"arc3,rad=-0.2\"))\n",
    "\n",
    "ax.annotate(\"Independence Day\", xy=('2012-7-4', 4250),  xycoords='data',\n",
    "            bbox=dict(boxstyle=\"round\", fc=\"none\", ec=\"gray\"),\n",
    "            xytext=(10, -40), textcoords='offset points', ha='center',\n",
    "            arrowprops=dict(arrowstyle=\"->\"))\n",
    "\n",
    "ax.annotate('Labor Day', xy=('2012-9-4', 4850), xycoords='data', ha='center',\n",
    "            xytext=(0, -20), textcoords='offset points')\n",
    "ax.annotate('', xy=('2012-9-1', 4850), xytext=('2012-9-7', 4850),\n",
    "            xycoords='data', textcoords='data',\n",
    "            arrowprops={'arrowstyle': '|-|,widthA=0.2,widthB=0.2', })\n",
    "\n",
    "ax.annotate('Halloween', xy=('2012-10-31', 4600),  xycoords='data',\n",
    "            xytext=(-80, -40), textcoords='offset points',\n",
    "            arrowprops=dict(arrowstyle=\"fancy\",\n",
    "                            fc=\"0.6\", ec=\"none\",\n",
    "                            connectionstyle=\"angle3,angleA=0,angleB=-90\"))\n",
    "\n",
    "ax.annotate('Thanksgiving', xy=('2012-11-25', 4500),  xycoords='data',\n",
    "            xytext=(-120, -60), textcoords='offset points',\n",
    "            bbox=dict(boxstyle=\"round4,pad=.5\", fc=\"0.9\"),\n",
    "            arrowprops=dict(arrowstyle=\"->\",\n",
    "                            connectionstyle=\"angle,angleA=0,angleB=80,rad=20\"))\n",
    "\n",
    "\n",
    "ax.annotate('Christmas', xy=('2012-12-25', 3850),  xycoords='data',\n",
    "             xytext=(-30, 0), textcoords='offset points',\n",
    "             size=13, ha='right', va=\"center\",\n",
    "             bbox=dict(boxstyle=\"round\", alpha=0.1),\n",
    "             arrowprops=dict(arrowstyle=\"wedge,tail_width=0.5\", alpha=0.1));\n",
    "\n",
    "# Label the axes\n",
    "ax.set(title='USA births by day of year (1969-1988)',\n",
    "       ylabel='average daily births')\n",
    "\n",
    "# Format the x axis with centered month labels\n",
    "ax.xaxis.set_major_locator(mpl.dates.MonthLocator())\n",
    "ax.xaxis.set_minor_locator(mpl.dates.MonthLocator(bymonthday=15))\n",
    "ax.xaxis.set_major_formatter(plt.NullFormatter())\n",
    "ax.xaxis.set_minor_formatter(mpl.dates.DateFormatter('%h'));\n",
    "\n",
    "ax.set_ylim(3600, 5400);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll notice that the specifications of the arrows and text boxes are very detailed: this gives you the power to create nearly any arrow style you wish.\n",
    "Unfortunately, it also means that these sorts of features often must be manually tweaked, a process that can be very time consuming when producing publication-quality graphics!\n",
    "Finally, I'll note that the preceding mix of styles is by no means best practice for presenting data, but rather included as a demonstration of some of the available options.\n",
    "\n",
    "More discussion and examples of available arrow and annotation styles can be found in the Matplotlib gallery, in particular the [Annotation Demo](http://matplotlib.org/examples/pylab_examples/annotation_demo2.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Multiple Subplots](04.08-Multiple-Subplots.ipynb) | [Contents](Index.ipynb) | [Customizing Ticks](04.10-Customizing-Ticks.ipynb) >\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.09-Text-and-Annotation.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
